Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from src.my_functions import *\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Case Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Type</th>\n",
       "      <th>Country</th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>Activity</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Injury</th>\n",
       "      <th>Fatal</th>\n",
       "      <th>Time</th>\n",
       "      <th>Investigator or Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018.06.25</td>\n",
       "      <td>25-Jun-2018</td>\n",
       "      <td>2018</td>\n",
       "      <td>Boating</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>Oceanside, San Diego County</td>\n",
       "      <td>Paddling</td>\n",
       "      <td>F</td>\n",
       "      <td>57.0</td>\n",
       "      <td>No injury to occupant, outrigger canoe and pad...</td>\n",
       "      <td>N</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>R. Collier, GSAF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018.06.18</td>\n",
       "      <td>18-Jun-2018</td>\n",
       "      <td>2018</td>\n",
       "      <td>Unprovoked</td>\n",
       "      <td>USA</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>St. Simon Island, Glynn County</td>\n",
       "      <td>Standing</td>\n",
       "      <td>F</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Minor injury to left thigh</td>\n",
       "      <td>N</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>K.McMurray, TrackingSharks.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018.06.09</td>\n",
       "      <td>09-Jun-2018</td>\n",
       "      <td>2018</td>\n",
       "      <td>Invalid</td>\n",
       "      <td>USA</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>Habush, Oahu</td>\n",
       "      <td>Surfing</td>\n",
       "      <td>M</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Injury to left lower leg from surfboard skeg</td>\n",
       "      <td>N</td>\n",
       "      <td>morning</td>\n",
       "      <td>K.McMurray, TrackingSharks.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018.06.08</td>\n",
       "      <td>08-Jun-2018</td>\n",
       "      <td>2018</td>\n",
       "      <td>Unprovoked</td>\n",
       "      <td>AUSTRALIA</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Arrawarra Headland</td>\n",
       "      <td>Surfing</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Minor injury to lower leg</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B. Myatt, GSAF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018.06.04</td>\n",
       "      <td>04-Jun-2018</td>\n",
       "      <td>2018</td>\n",
       "      <td>Provoked</td>\n",
       "      <td>MEXICO</td>\n",
       "      <td>Colima</td>\n",
       "      <td>La Ticla</td>\n",
       "      <td>Free diving</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lacerations to leg &amp; hand shark PROVOKED INCIDENT</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A .Kipper</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Case Number         Date  Year        Type    Country             Area  \\\n",
       "0  2018.06.25  25-Jun-2018  2018     Boating        USA       California   \n",
       "1  2018.06.18  18-Jun-2018  2018  Unprovoked        USA          Georgia   \n",
       "2  2018.06.09  09-Jun-2018  2018     Invalid        USA           Hawaii   \n",
       "3  2018.06.08  08-Jun-2018  2018  Unprovoked  AUSTRALIA  New South Wales   \n",
       "4  2018.06.04  04-Jun-2018  2018    Provoked     MEXICO           Colima   \n",
       "\n",
       "                         Location     Activity Sex   Age  \\\n",
       "0     Oceanside, San Diego County     Paddling   F  57.0   \n",
       "1  St. Simon Island, Glynn County     Standing   F  11.0   \n",
       "2                    Habush, Oahu      Surfing   M  48.0   \n",
       "3              Arrawarra Headland      Surfing   M   NaN   \n",
       "4                        La Ticla  Free diving   M   NaN   \n",
       "\n",
       "                                              Injury Fatal       Time  \\\n",
       "0  No injury to occupant, outrigger canoe and pad...     N  afternoon   \n",
       "1                         Minor injury to left thigh     N  afternoon   \n",
       "2       Injury to left lower leg from surfboard skeg     N    morning   \n",
       "3                          Minor injury to lower leg     N        NaN   \n",
       "4  Lacerations to leg & hand shark PROVOKED INCIDENT     N        NaN   \n",
       "\n",
       "           Investigator or Source  \n",
       "0                R. Collier, GSAF  \n",
       "1  K.McMurray, TrackingSharks.com  \n",
       "2  K.McMurray, TrackingSharks.com  \n",
       "3                  B. Myatt, GSAF  \n",
       "4                       A .Kipper  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('./output/Sharks_attacks_cleaned.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis 1: Most of the provoked shark attacks are by Americans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our approach will be create a different dataframe that only records provoked cases of shark attacks and the compare it with the total number of sharks attacks registered for each country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb63c870fd0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "list1=list(dict(data.Country.loc[data.Type=='Provoked'].value_counts()).keys())\n",
    "list2=list(dict(data.Country.loc[data.Type=='Provoked'].value_counts()).values())\n",
    "\n",
    "dictionary=dict(data.Country.value_counts())\n",
    "          \n",
    "                \n",
    "df=pd.DataFrame(data=[list1,list2],index=['Country','total_cases']).T\n",
    "\n",
    "\n",
    "\n",
    "busqueda_población(df,dictionary)\n",
    "\n",
    "df['Relative frequency']=df.total_cases/df.Total_population_of_country*100\n",
    "\n",
    "df=df.sort_values(by='Relative frequency')\n",
    "df[df.total_cases>4].plot.bar(x='Country',y='Relative frequency')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.33684851754672"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Relative frequency'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hypothesis: Most of the provoked shark attacks are by Americans proves to be wrong. The country with a significative number of registered cases (above 4) have a mean value around 33% event most of the are near 10%. The increment in the total mean value is due to the contribution of United Kingdom and England (which is a region of Unitd Kingdom), which have a clearly bigger frequency of provoked cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis 2: Ages are not uniformly distributed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't know how will be distributed the number of incidences with the Age, but we are totally sure that they are not uniform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "list1=list(dict(data.Age[data.Age>=0].value_counts()).keys())\n",
    "list2=list(dict(data.Age[data.Age>=0].value_counts()).values())\n",
    "df=pd.DataFrame(data=[list1,list2],index=['Age','Total cases']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb640f14550>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='Age',y='Total cases',c='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hypothesis 2: Ages are not uniformly distributed is True. The distribution of ages is clearly left-skewed, showing that younger people are more prone to have incidents involing sharks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis 3: Sharks tends to attack  in the afternoon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=data[data.Time!='NaN']\n",
    "\n",
    "list1=list(dict(data.Time.loc[data.Time!='NaN'].value_counts()).keys())\n",
    "list2=list(dict(data.Time.loc[data.Time!='NaN'].value_counts()).values())\n",
    "\n",
    "df=pd.DataFrame(data=list2,index=list1, columns=['Fatal cases'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6417c2208>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.pie(y='Fatal cases',figsize=(8,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hypothesis 3: Sharks tends to attack  in the afternoon is True. There are more attacks in the afternoon than in the morning or during the night. Even thought, there is no a big difference in number between attacks in the morning or in the afternoon."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}